Graphs as Models of Large-Scale Biochemical Organization

“…It is important to note that the average clustering coefficient of biological networks is higher by 2 orders of magnitude than that of the random networks of the same size and the same average vertex degree. Thus, for the protein−protein interaction network of Saccharomyces cerevisiae , < C > = 0.142, whereas for the corresponding random network, it is only 0.001 39 . The clustering effects in molecular structures have not been studied, due to the fact that the highly strained tri-membered atomic rings are rarely stable.…”

Section: Clustering Coefficientmentioning

confidence: 99%

“…Thus, for the protein-protein interaction network of Saccharomyces cer-eVisiae, <C> ) 0.142, whereas for the corresponding random network, it is only 0.001 39. 29 The clustering effects in molecular structures have not been studied, due to the fact that the highly strained tri-membered atomic rings are rarely stable. In crystallography, however, the abundance of tetrahedral structures makes cluster analysis quite relevant.…”

Section: Clustering Coefficientmentioning

confidence: 99%

From Molecular to Biological Structure and Back

Bonchev

Buck

2007

J. Chem. Inf. Model.

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A comparative analysis of the topological structure of molecules and molecular biology networks revealed both similarity and differences in the methods used, as well as in the essential features of the two types of systems. Molecular graphs are static and, due to the limitations in atomic valence, show neither power distribution of vertex degrees nor "small-world" properties, which are typical for dynamic evolutionary networks. Areas of mutual benefits from an exchange of methods and ideas are outlined for the two fields. More specifically, chemical graph theory might make use of some new descriptors of network structure. Of interest for quantitative structure-property relationship/quantitative structure-activity relationship and drug design might be the conclusion that descriptors based on distributions of vertex degrees, distances, and subgraphs seem to be more relevant to biological information than the single-number descriptors. The network concepts of centrality, clustering, and cliques provide a basis for similar studies in theoretical chemistry. The need of dynamic theory of molecular topology is advocated.

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Graphs as Models of Large-Scale Biochemical Organization

Cited by 2 publications

References 33 publications

Network models in the study of metabolism

Network models in the study of metabolism

From Molecular to Biological Structure and Back

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